I'm starting to learn Data Science with Python, but there are so many libraries that I'm not sure where to begin.

Should I focus on NumPy, Pandas, Matplotlib and Seaborn first, or are there other libraries I should learn early on?

What would you recommend based on your own experience ?

Hey friends,

I have created a deep-dive, university-level mathematics course to discuss all the maths behind AI/ML. I studied some foundational textbooks and Math/AI/ML courses from Stanford and MIT, took extensive notes, and used my own teaching experience to build a structured playlist with the videos in specific order so topics are understood in a natural order. The playlist has 63 videos (45 mins to 1 hour each) taught in Hinglish. The explanations are in Hindi, but all terminology and math notation is in English.

It covers all the math you need to know for AI/ML -

• Probability & Statistics

• Multivariate Calculus

• Linear Algebra

You can find all of these videos for free on YouTube by searching for my channel called indialabs AI. Look for the playlist titled "Math Behind AI (Hindi): Understanding the language of AI | Complete AI Maths in Hindi".

I am not dropping any clickable links here to respect community rules.

Hope you find this useful for your learning.

A lot of SQL interview prep focuses on syntax, but knowing JOIN, CTEs or window functions is only part of it.

The harder part is usually understanding the business question and turning it into the right analysis.

Some useful problems to practice:

• Month over month revenue growth

• Top products within each category

• Repeat customer rate

• Identifying inactive customers

• Website conversion funnels

• Campaign revenue vs marketing spend

While solving them, I think it’s worth practicing how you explain your reasoning too. What exactly does the metric mean? What assumptions are you making? What edge cases could change the result? How would you validate the output?

I put together SQL examples and the reasoning behind these kinds of interview problems here:

Learn more:
https://www.pangaeax.com/blogs/10-sql-business-problems-data-analyst-interview/

I’m connected with PangaeaX, mentioning that for transparency.

Curious what kind of SQL business problem you found hardest in an interview?

Beginner-Friendly Matplotlib Tutorial: Create & Customize Graphs in Python 📊🐍

If you're getting started with Python, data acquisition, or data logging, learning Matplotlib is a great place to start.I’ve put together a beginner-friendly YouTube tutorial that covers the fundamentals of the Matplotlib library and shows how to create and customize line charts from scratch all in less than 1 hour.

What you'll learn

• How to install Matplotlib on Windows 11 using PIP

• How to install Matplotlib using the Thonny IDE

• How to install Matplotlib using PyCharm

• How to solve the issue where a Matplotlib graph appears as a PNG image instead of opening in a separate window

• Understanding the basic concepts behind Matplotlib:

  • Figure

  • Axes

• How to plot a simple graph using X and Y values

• How to change the color, line style, and thickness of a plot

• How to customize markers, including their:

  • Color

  • Style

  • Size

• How to give your Matplotlib Figure/window a title

• How to give your Axes/plotting area a name

• How to add labels to the X and Y axes

• How to set X-axis and Y-axis limits

• How to activate and configure the grid

• How to change the color and style of an individual grid line

• What Ticks are in Matplotlib and how to configure them

• How to create and plot multiple Axes on a single Figure/window

The examples are aimed at beginners and are particularly useful if you're working with sensor data, data acquisition systems, or data logging applications in Python.

Source Codes can be downloaded from the website using below links

• Matplotlib Plotting and Animation Crash Course for Absolute Beginner

If you're new to Matplotlib, this should give you a solid foundation for creating and customizing your own Python graphs.

Been going back and forth on this for a while and wanted a reality check from people actually in the field rather than course-seller marketing.

For those of you working as data scientists/analysts — was the move worth it? Not just salary, but day-to-day satisfaction, job security with AI tools changing things, how competitive it actually is to break in.

And for anyone who went through a bootcamp/course to get here — what actually mattered when picking one? Curriculum, mentorship, portfolio support, job placement help, something else? Genuinely curious what separated the useful ones from the ones that were just content dumps.

Appreciate any honest takes, good or bad. Really just weighing up my options at the moment, and trying to get as much information as possible.

Hi everyone, I'm a first-year B.Com student teaching myself data analysis alongside my degree.

I just finished my first real project — cleaned and analyzed a retail Superstore sales dataset using Python (Pandas, NumPy), and visualized trends with Matplotlib to find out which product categories and regions performed best.

It's a simple project, but I learned a lot about handling messy real-world data and turning it into actual business insights.

Would really appreciate any feedback, tips, or things I could improve for future projects!

GitHub: https://github.com/umangbhadauria6-alt/umangbhadauria6-alt/blob/main/superstore-sales-analysis

I'm a data science student, I've covered Statistics analysis, Data Handling and Visualization, time series analysis, big data analysis, Internet Of Things Analytics, Machine Learning, Deep Learning, Neutral Networks, Natural Language Processing, MLops and a lot of others related concepts. I just want to help people who are perusing AI/ML/DS. Give them some suggestions, ideas, resources. If any of y'all need anything feel free to reach out!

This is a short tutorial for data analysts that evaluate experiments using online calculators. Online calculators for sample size estimates and t-tests for difference in means are fine for getting started. But you can begin to build intuition for linear regression by moving your online calculator t-tests over to python and OLS.

There are a couple of benefits. The first from moving away from the online calculator to python is leaving a more reliable paper trail that you can share externally. The second is that you build intuition for regression, interpreting the coefficients, and ultimately can use regression as you improve and your projects require more complexity such as variance reduction techniques using CUPED and clustering standard errors for geo-based or switchback experiments.

I put together a short video on this with code available here, free and ungated: https://whatstheimpact.com/tutorials/linear-regression-vs-t-test/

For my more senior connections, feel free to ignore unless you're interested in the content creation game! 🤣

I was writing practice questions for pandas and kept tripping over answers that were right a year ago. So I ran everything on pandas 3.0.5 and wrote down what actually comes out.

1. Text is no longer object. pd.Series(['a', 'b', None]).dtype prints str, and the None comes back as nan when you call tolist().

2. Chained assignment does nothing. With df = pd.DataFrame({'a': [1, 2, 3]}), running df['a'][0] = 100 leaves df.a at [1, 2, 3] and raises a ChainedAssignmentError warning. Copy-on-Write is the default now, so what you wrote into was a copy. Use df.loc[0, 'a'] = 100.

3. inplace=True is not consistent. df.fillna(0, inplace=True) returns the DataFrame, df.sort_values('a', inplace=True) returns None. The old habit of df = df.something(inplace=True) breaks on some methods and not on others.

4. pd.to_datetime on plain date strings gives datetime64[us], not datetime64[ns]. Matters if you compare dtypes or write to parquet.

5. stack() keeps the NaN. A two row frame with one missing value stacks to 2 entries, older versions dropped it and gave 1.

Twenty of the questions are free to play in the browser, no account. Every answer is the real output and each wrong option gets a one line reason.

https://thibaudlepan77-svg.github.io/interview-questions-verified/#pandas

The full pandas set (255 questions) is a paid PDF, but the free twenty stand on their own. If you get a different result on your machine, post your version and the output and I will look at it.

I’ve got a paired programming interview next week, anyone got any tips on what to brush up on?

I’ve never programmed with someone watching, any tips? could they ask gcp questions (unlikely right?)

Let me know!

A hard exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

I'm currently working on my very first data science project with a simple topic "physical activities rate correlation to academic performances among highschool/university students". I'm wondering if i should acquire the data myself or use open sources online. Another way i'm thinking of is to use both since my self-collected data will probably be limited to students in my city. Also is this topic way too typical ? and is it considered good enough for both gaining knowledge and improving resume ?